Incremental precision networks using residual inference and fine-grain quantization

ABSTRACT

One embodiment provides for a computer-readable medium storing instructions that cause one or more processors to perform operations comprising determining a per-layer scale factor to apply to tensor data associated with layers of a neural network model and converting the tensor data to converted tensor data. The tensor data may be converted from a floating point datatype to a second datatype that is an 8-bit datatype. The instructions further cause the one or more processors to generate an output tensor based on the converted tensor data and the per-layer scale factor.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No.15/869,515, filed Jan. 12, 2018, which claims priority to IndiaProvisional Application No. 201741015052 filed on Apr. 28, 2017, whichis hereby incorporated by reference.

This application also claims priority to U.S. Provisional PatentApplication Ser. No. 62/501,800, filed on May 5, 2017, which is herebyincorporated herein by reference.

FIELD

Embodiments relate generally to data processing and more particularly tomachine learning processing via a general-purpose graphics processingunit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data: however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the features of the present invention can be understood indetail, a more particular description of the invention may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of the scope of all embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs is communicatively coupled to a plurality of multi-core processors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a machine learning software stack, according to anembodiment;

FIG. 7 illustrates a highly parallel general-purpose graphics processingunit, according to an embodiment;

FIG. 8 illustrates a multi-GPU computing system, according to anembodiment;

FIG. 9A-9B illustrate layers of exemplary deep neural networks;

FIG. 10 illustrates an exemplary recurrent neural network;

FIG. 11 illustrates training and deployment of a deep neural network;

FIG. 12 is a block diagram illustrating distributed learning;

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model;

FIG. 14A illustrates a schematic diagram of a ternary residualinferencing network;

FIG. 14B illustrates operations of logic to perform residual ternaryinference, according to embodiments described herein;

FIG. 14C-14F illustrates techniques for fine-grained quantization,according to embodiments described herein.

FIG. 15A illustrates a schematic diagram of logic to perform localoptimization;

FIG. 15B illustrates logic to perform local optimization based onternary representation;

FIG. 16 is a block diagram of a processing system, according to anembodiment;

FIG. 17 is a block diagram of a processor according to an embodiment;

FIG. 18 is a block diagram of a graphics processor, according to anembodiment;

FIG. 19 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 20 is a block diagram of a graphics processor provided by anadditional embodiment;

FIG. 21 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments;

FIG. 22 is a block diagram illustrating graphics processor instructionformats according to some embodiments;

FIG. 23 is a block diagram of a graphics processor according to anotherembodiment;

FIG. 24A-24B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 25 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 26 is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 27 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment;

FIG. 28 is a block diagram illustrating an additional exemplary graphicsprocessor, and

FIG. 29 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral-purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1 . For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1 , according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2A) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110A-110B ofFIG. 1 , routed for further processing by the processor(s) 102, orrouted for further processing by one of the processing entities withinthe parallel processor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can executed via a single SIMD instruction.For example and in one embodiment, eight SIMT threads that perform thesame or similar operations can be executed in parallel via a singleSIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each of multi-core processor 405 and multi-coreprocessor 406 are communicatively coupled to a processor memory 401-402,via memory interconnects 430A-430B, respectively, and each GPU 410-413is communicatively coupled to GPU memory 420-423 over GPU memoryinterconnects 450A-450D, respectively. The memory interconnects430A-430B and 450A-450D may utilize the same or different memory accesstechnologies. By way of example, and not limitation, the processormemories 401-402 and GPU memories 420-423 may be volatile memories suchas dynamic random access memories (DRAMs) (including stacked DRAMs),Graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or High Bandwidth Memory(HBM) and/or may be non-volatile memories such as 3D XPoint or Nano-Ram.In one embodiment, some portion of the memories may be volatile memoryand another portion may be non-volatile memory (e.g., using a two-levelmemory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, M is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, M (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherence bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of registers445 of the accelerator integration slice 490.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1 .The various parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1 ), to be displayed on the one ormore display device(s) 110A-110B or for further processing by one of theone or more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a highly parallel general-purpose graphics processingunit 700, according to an embodiment. In one embodiment thegeneral-purpose processing unit (GPGPU) 700 can be configured to beparticularly efficient in processing the type of computational workloadsassociated with training deep neural networks. Additionally, the GPGPU700 can be linked directly to other instances of the GPGPU to create amulti-GPU cluster to improve training speed for particularly deep neuralnetworks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-706H. The compute clusters706A-706H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-706H.

The GPGPU 700 includes memory 714A-714B coupled with the computeclusters 706A-H via a set of memory controllers 712A-712B. In variousembodiments, the memory 714A-714B can include various types of memorydevices including dynamic random access memory (DRAM) or graphics randomaccess memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory or 3D stackedmemory, including but not limited to high bandwidth memory (HBM).

In one embodiment each compute cluster 706A-706H includes a set ofgraphics multiprocessors, such as the graphics multiprocessor 325 ofFIG. 3A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example and in one embodiment atleast a subset of the floating point units in each of the computeclusters 706A-H can be configured to perform 16-bit or 32-bit floatingpoint operations, while a different subset of the floating point unitscan be configured to perform 64-bit floating point operations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment the GPGPU 700 includes an I/O hub709 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment the GPU link 710 couples with a high speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 700 includes fewer of the computeclusters 706A-706H relative to the training configuration. Additionallymemory technology associated with the memory 714A-714B may differbetween inferencing and training configurations. In one embodiment theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800, according to anembodiment. The multi-GPU computing system 800 can include a processor802 coupled to multiple GPGPUs 806A-D via a host interface switch 804.The host interface switch 804, in one embodiment, is a PCI expressswitch device that couples the processor 802 to a PCI express bus overwhich the processor 802 can communicate with the set of GPGPUs 806A-D.Each of the multiple GPGPUs 806A-806D can be an instance of the GPGPU700 of FIG. 7 . The GPGPUs 806A-D can interconnect via a set ofhigh-speed point-to-point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-806D via a dedicatedGPU link, such as the GPU link 710 as in FIG. 7 . The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-806D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 9A-9B illustrate an exemplary convolutional neural network. FIG. 9Aillustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations are make use of fully connected layers 908. For example,in some implementations the convolutional layer 906 can generate outputfor the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolutional layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 916 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 916defines a set of linear activations that are processed by successivestages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0, x), such that the activation is thresholded at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and 12-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function ƒ(x)=max(0, x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1102. Various training frameworks1104 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 604 of FIG. 6 maybe configured as a training framework 1104. The training framework 1104can hook into an untrained neural network 1106 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations, forexample, to generate a result 1114 based on inference or classificationoperations performed on new data 1112.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1108 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly parallel general-purpose graphicsprocessing unit 700 as in FIG. 7 . As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly parallel general-purpose graphics processing unit 700 of FIG. 7and the multi-GPU computing system 800 of FIG. 8 . On the contrary,deployed machine learning platforms generally include lower powerparallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within the highlyparallel general-purpose graphics processing unit 700. The computeclusters within the GPGPU 1306 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1306 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

Incremental Precision Networks Using Residual Inference

In one embodiment, inference operations on a neural network areperformed using multiple passes at a relatively low precision,accumulating the partial output computed using residual weights. Anumber of residual accumulations can be obtained by observing theprobability distribution of the layer or of the network as a whole. Suchtechnique enables multi-precision inference to be performed oncomputational hardware that lacks support for multiple precisions. Inone embodiment, to improve the accuracy of ternary networks though localoptimization using limited number of training samples, information fromthe training samples can be used to minimize the Euclidian distancebetween the full-precision and ternary weights.

Threshold-based Ternarization

In one embodiment, a threshold based ternarization algorithm is used inwhich a threshold is computed for magnitude of the weights. Any weightwith magnitude less than the computed threshold is set to zero, whilethe sign of the weight is maintained. Using the computed threshold, arepresentation of any weight in the set of weights can be generatedusing one of three values: {0, +1, −1}. A scaling factor, which is apositive real number, can be computed that brings the ternary valuesclose to the original values.

In one embodiment, the scaling factor can be determined by minimizingthe

loss between pre-trained weights and the ternary weights, where the

loss is the least squared error function, which can be defined as:

$\ell_{2} = {\sum\limits_{i = 0}^{n}\left( {y_{i} - {h\left( x_{i} \right)}} \right)^{2}}$

Threshold based ternarization can be performed using a variety oftechniques known in the art or as described in further detail below. Insome embodiments, a single threshold and scale factor can be used toternarize an entire set of weights for a neural network or a layer ofthe neural network. Some embodiments can perform fine-grainedternarization of pre-trained data, in which different portions of theweight data are ternarized using thresholds and/or scale factors thatcan differ across the set of weights. In one embodiment, fine-grainedternarization can be performed using scale factors and/or thresholdsthat differ based on the data distribution of the weight data, withdifferent subsections of the set of weights having different thresholdsor scale factors. For example, a first subsection of weight data havinga first data distribution profile can have a first threshold and scalefactor, while a second subsection of weight data having a second datadistribution profile can have a second threshold or scale factor.

Ternary Residual Inference

The error associated with the threshold-based ternarization can bereduced using a residual ternary representation by compensating for theloss in accuracy due to the low precision of the ternarized weights.Using residual representation can compensate for the loss in accuracydue to the low precision of the ternary representation, enabling lowprecision inference to be performed using ternary weights withoutrequiring a re-training operation.

FIG. 14A illustrates a schematic diagram 1400 of ternary residualinference in contrast with full precision and ternary inference.Embodiments described herein provide machine learning inferencingaccelerators that are configurable between a full precision setting1402, a ternary setting 1404, and a ternary residual setting 1406.

For the full precision setting 1402, computational logic performs aconvolution operation (⊙) 1403 using input weights (x) 1401 and fullprecision, pre-trained weights 1412 without any quantization performedon the weights. The pre-trained weight 1412 are maintained at theoriginal precision at which the network was trained, and may be anynon-binary or non-ternary precision, such as, but not limited to 8-bit,16-bit, or 32-bit precision. Performing full precision convolution forinferencing operations may yield higher accuracy relative to the ternarysetting 1404, but with higher cost in terms of computational, memory,bandwidth, and storage resources.

The ternary setting 1404 enables computational operations to beperformed using input data 1401 and ternarized weights 1414 as describedherein. Full-precision pre-trained weights 1412 can be ternarized usingtechniques described herein, including threshold based ternarizationand/or fine-grained ternarization. In one embodiment, the ternarizationcan be automatically performed by computational logic based onconfiguration settings and/or instruction parameters used to configurecomputation operations for a neural network. A convolution operation canthen be performed using input data 1401 and the ternarized weights 1414.Where α is the ternary scale factor, convolution using ternarizedweights can be approximated using an XOR operation 1403, such that:

x*W≈(x⊕Ŵ)α

Embodiments described herein additionally provide a ternary residualsetting 1406, in which inference convolution operation can beapproximated via an XOR operation 1403 using input data 1401 andternarized weights 1414. With the ternary residual setting 1406,inferencing accuracy can be improved via residual ternary refinementusing residual weight values 1416.

FIG. 14B illustrates operations of logic 1420 to perform ternaryinference, according to embodiments described herein. The logic 1420 canbe performed by or implemented within computational logic, computeunits, or other execution resources within graphics processors ormachine-learning accelerator logic as described herein. In oneembodiment, the logic can be implemented in part via compute shadersexecuted via the execution resources described herein.

In one embodiment, the logic 1420 can cause computational logic toreceive a group of full-precision pre-trained weights, as shown at block1422. The grouping of pre-trained weights can be performed based on aclustering algorithm or any other heuristic.

The logic 1420 can then cause the computational logic to performoperations to ternarize the group of pre-trained weights, as shown atblock 1424. The ternarization can be performed using threshold basedternarization as described above, in which a threshold and scalingfactor is determined such that the

loss between pre-trained weights and the ternary weights is minimized.

The logic 1420 can additionally cause computational logic to determineif the group of pre-trained weights is eligible for a residualcomputation, as shown at block 1426. Determining eligibility can beperformed using a variety of heuristics. In one embodiment, a group isdetermined to be eligible for residual computation and residualternarization if the group has a large dynamic range, such that thereexists a large difference between the maximum element and the minimumelement. A group can also be determined to be eligible for a residualcalculation if the group is a heavy-tailed distribution, such that mostof the elements are small in value, while a few elements have very largevalues. Other determination factors can also be used. If the group ofpre-trained weights is not eligible for residual calculation, as shownat block 1427, operations of the logic 1420 can proceed to block 1434and store the group of ternarized weights, which were ternarized atblock 1424.

If the group of pre-trained weights is eligible for residual computationat block 1427, the logic 1420 can cause computational logic to proceedto block 1428 to compute residual weights for the group. The residualweights can be computed based on the difference between the fullprecision pre-trained weights of the group and the ternary weightscomputed at block 1424. Operations of the logic 1420 can continue, atblock 1430, to ternarize the residual weights. At block 1432, the logic1420 can store the ternarized residual weights. The logic 1420 can thencause computational logic to store the ternarized weights at block 1434in conjunction with storing the ternarized residual weights at block1432, or to store only the ternarized weights if the logic 1420 is tobypass computation of residual weights for the group at block 1427.

Once the group of ternarized weights and possible group of ternarizedresidual weights are stored, computation operations can be performed toperform the necessary inference operations (e.g., convolution, etc.) onthe ternary weights using input data. In some embodiments, the conceptof residual inference described herein can be extended to otherlow-precision inference settings. Residual ternary refinement can beperformed in an iterative manner to significantly reduce the relativeerror between full-precision and ternary weights. With a sufficientnumber of iterations, the relative error can extend below 0.1%.

Fine-Grained Quantization of Neural Networks

Embodiments described herein provide for a fine-grained quantizationmethod for ternarizing pre-trained full precision models that constrainactivations to 8-bits. Applying one method provided by one embodiment onResnet-101 using a group size of N=4 can achieve 73.8% Top-1 accuracy,within 4% of the full precision result while eliminating 75% ofmultiplications. Using slightly larger subgroups of N=8, one can replace87.5% of multiplications while losing an addition 2% accuracy, achieving71.7% Top-1 accuracy in one embodiment. Using this method, minimal lossin classification accuracy may be achieved on state-of-the-arttopologies without additional training in some embodiments, enabling afull 8-bit inference pipeline. Further, a theoretical formulation formsthe basis for a higher quality solution with this approach. The originalweight tensor is ternarized in groups of N weights. With a group size ofN=64, about 99% of the multiplications may be eliminated; however, thisintroduces a significant drop in accuracy, which necessitates finetuning the parameters (re-training) at lower precision. A finalquantized model can execute on a full 8-bit compute pipeline using 2-bitweights, having the potential for up to 16× improvement in performancecompared to baseline full-precision models.

While ternary neural networks are described herein, embodiments may alsobe configured to perform fine-grained quantization to binary neuralnetworks. In one embodiment, a quantization to a ternary neural networkcan be performed, and an additional quantization can be performed to abinary neural network, where a portion of the zero values are convertedin to positive sign values and a portion of the zero values areconverted into negative sign values. The binary weights may be stored ina single bit, rather than the 2-bits used for ternary neural networks.Some embodiments may also be configured to enable quantization to fourvalues, which may be stored in 2-bits. Embodiments may also beconfigured to enable quantization to sixteen values, which may be storedin 4-bits.

Today's deep learning models achieve state-of-the-art results on a widevariety of tasks including Computer Vision, Natural Language Processing,Automatic Speech Recognition and Reinforcement Learning. Mathematically,this involves solving a non-convex optimization problem with order ofmillions or more parameters. Solving this optimization problem—alsoreferred to as training the neural network—is a compute-intensiveprocess that, for current state-of-the-art networks, requires days toweeks. Once trained, the network evaluates a function on specific inputdata—referred to as inference.

While the compute intensity for inference is much lower than that oftraining, owing to the fact that inference is done on a large number ofinput data, the total computing resources spent on inference is likelyto dwarf those spent on training. Because of the large and somewhatunique compute requirements for both deep learning training andinference operations, of customized low precision arithmetic andspecialized hardware may run these computations more efficiently. Mostof these cases requires partial or full training of network in lowprecision, Training at low precision allows for the network toimplicitly learn the low precision representation (along with theinherent noise); however, it introduces significant resource overheadthere can be prohibitive for many resource-constrained applications,specifically those involving edge devices.

Reducing precision for both weights and activations has significantpower-performance implications on system design, enabling higher computedensity while reducing pressure on the memory sub-system. Reducing thesize of activations leads to more efficient utilization of the availablebandwidth and opens up the possibility of achieving up to 16×improvement in throughput. Further, with widespread adoption of deeplearning across various applications like autonomous driving andaugmented reality, there is an increased demand for inference tasks tobe efficiently done on edge devices. These system and applicationrequirements may be address by a full lower precision inferencepipeline.

In one embodiment, a sub 8-bit inference pipeline uses ternary weightsand 8-bit activations, with minimal or no re-training. Thefull-precision weights may be converted to low-precision, such that theelement-wise distance between full-precision and low-precision weightsis small. Consequently, the low-precision weights remain in theneighborhood of pre-trained full-precision weights in the search space.

Based on an improved theoretical formulation, a fine-grainedquantization method converts pre-trained weights a ternaryrepresentation, in one embodiment with minimal loss in test accuracy,without re-training. With 8-bit activations and ternary weights (8-2),using groups of N=4 weights, Top-1 accuracy of 73.85% for Resnet-101 and67.30% for Resnet-50 can be achieved. For a group size of N-KxK weights,the number of (8-bit) multiplications may be reduced to one in every Nadditions, thus significantly reducing computation complexity.Full-precision trained weights W may be converted to ternary scalingvalues {−α, 0, +α}, α≥0, without re-training. A threshold based approachusing a threshold Δ that is greater than zero produces values that aresimilar to i-th element Ŵ_(i)=sign(W_(i)), if |W_(i)|>Δ, and 0otherwise. Then, the element-wise error is

${E\left( {\alpha,\Delta} \right)} = {{{W - {\alpha\hat{W}}}}\frac{2}{F}}$

and an optimal ternary representation

${{\alpha*\hat{W}} \approx {W{is}\alpha^{*}}},{\Delta^{*} = {\underset{{\alpha \geq 0},{\Delta > 0}}{\arg\min}{E\left( {\alpha,\Delta} \right)}}},$

such that Ŵ_(i) ∈ {−1,0, +1}, i=1,2, . . . , n, where n is the size of W(W ∈

).

Weights that learn different types of features may follow differentdistributions. Combining all the weights together represents a mixtureof various distributions, and ternarizing them using a single threshold(Δ) and scaling factor magnitude (α) may not preserve the distributionsof individual weights. Consequently, many weights may be approximatedpoorly (if not totally pruned out), causing the loss of valuable learnedinformation.

Computing separate thresholds (Δ) and scaling factors (α) for eachweight compensates for information loss and better preserves theunderlying distributions. However, such approach, despite showingsignificant improvement in accuracy, typically involves a larger numberof multiplications leading to a less efficient implementation. Afine-grained quantization approach creates groups of weights andternarizes each group independently. Consider the weights represented asa vector W ∈ R^(n). The set I of n indices is partitioned into kdisjoint subsets, c₁, c₂, . . . , c_(k), with cardinality |c_(i)|=n_(i),such that c_(i) ∩ c_(j)=0, ∪_(i) c_(i)=1, Σ_(i)n_(i)=n. One candecompose W into k orthogonal vectors W^((i)) ∈

, i=1, . . . ,k, where j-th component W_(j) ^((i))=W_(j) if j ∈ c_(i),otherwise 0. Clearly, Σ_(i) W^((i))=W; then ternarize each orthogonalcomponent W^((i)) as α_(i)Ŵ^((i)), where components of Ŵ^((i)) are in{−1,0, +1}. Threshold-based approach never turns zero to non zero, andthe following orthogonality holds.

W ^((i)) ⊥W ^((j)) Ŵ ^((i)) ⊥Ŵ ^((j)) W ^((i)) ⊥Ŵ ^((j)), for i≠j

It follows that, (W^((i))-α_(i)Ŵ^((i)))⊥(W^((j))−α_(j)Ŵ^((j))), for,i≠j. Then we have

${{W - {\sum\limits_{i}{\alpha_{i}{\hat{W}}^{(i)}}}}}_{F}^{2} = {\sum\limits_{i}{{W^{(i)} - {\alpha_{i}{\hat{W}}^{(i)}}}}_{F}^{2}}$

This leads to the following optimization. For a given group of k filters{W^((i))}, i=1, . . . , k, and W_(j) ^((i))∈{−1,0,+1}, ∀_(j),

α_(i)^(*), …, α_(k)^(*), Ŵ^((1)*), …, Ŵ^((k)*) ${= {\begin{matrix}{\arg\min} \\{\alpha_{i}{\hat{W}}^{(i)}}\end{matrix}{{W - {\sum\limits_{i}{\alpha_{i}{\hat{W}}^{(i)}}}}}_{F}^{2}}},$${= {\sum\limits_{i}{\begin{matrix}{\arg\min} \\{\alpha_{i}{\hat{W}}^{(i)}}\end{matrix}{{{\hat{W}}^{(i)} - {\alpha_{i}{\hat{W}}^{(i)}}}}_{F}^{2}}}},$

Therefore, k independent sub-problems need to be solved, where thesolutions to the sub problems are thread-parallel and easilyparallelizable. This formulation allows a better ternary approximationto the original full-precision weights, ensuring that they remain withina neighborhood of the original solution in the complex search space ofparameters, despite no re training. Consequently, the full-precisionsolution and the ternary counterpart generalize in a similar manner.

We can solve each sub-problem using the following approach. LetI_(Δ)={i: |W_(i)|>Δ}. Then,

$a^{*} = {{{\left\langle {W,\hat{W}} \right\rangle/}❘{❘\hat{W}❘}_{F}^{2}} = {\left( {\sum\limits_{i \in I_{\Delta}}{❘W_{i}❘}} \right)/{❘I_{\Delta}❘}}}$$\left. {\Delta^{*} = {\arg{\max\limits_{\Delta > 0}\left( \sum\limits_{i \in I_{\Delta}} \right)}{❘W_{i}❘}}} \right)^{2}/{❘I_{\Delta}❘}$

where

. . .

denotes the inner product. For full-precision learned weightsexponential or heavy-tailed distributions (e.g. power law) or even amixture of distributions better model the probability of magnitude ofthe weights. Assuming that the magnitude of the learned weights followsexponential distribution with parameter λ, derive the optimal Δ* isderived from the following lemma.Lemma 1. Using the above notations, if |W_(i)|˜exp(λ), then:

${{\left. {\Delta^{*} = {\arg{\max\limits_{\Delta > 0}\left( \sum\limits_{i \in I_{\Delta}} \right)}{❘W_{i}❘}}} \right)^{2}/{❘I_{\Delta}❘}} \approx \frac{1}{\lambda}} = {\sum\limits_{i}{{❘W_{i}❘}/n}}$

Applying a Kolmogorov-Smirnov (K-S) test as a goodness-of-fit measure toidentify an appropriate reference distribution (choosing betweenGaussian and exponential), and to find Δ*. A heavy-tailed distributionis approximated by an exponential one by pruning out some of the smallerelements. This gives an exponential approximation with smaller λ.Further, maximum likelihood functions may be used to estimate theparameters of such distributions. For Gaussian, N (0, σ), estimated{circumflex over (σ)}=√{square root over (Σ_(i=1)^(n)|W_(i)|²/n=rms)}(W) and for exponential case, estimated parameter{circumflex over (λ)}=Σ_(i) ^(n)|W_(i)|²/n. Based in such analysis, animprovement of ternary error over Gaussian assumption may be realized.

A mechanism groups full-precision weights to be ternarized. Solutionsthat use arbitrarily grouped filters would have to perform partialoutput accumulations by gathering all the weights that use a common a toachieve reasonable computational efficiency. One advantageous goal is tofind a solution that achieves higher efficiency on general purposehardware by minimizing the number of multiplications while alsoattaining higher accuracy.

Partitioning weights along input channels C achieves better results.Individual elements at the same location from multiple filters along thesame input channel C, have less variance because they learn similarfeatures. Since the elements along the same input channel C accumulateto the same output feature, this layout is also amenable tovectorization along K. Static groups of weights from contiguous weightsalong the C dimension may be used.

FIG. 14C shows an exemplary group 1440 of 3×3 filters. The group 1440 offilters contains (R×S) sub groups of n elements from contiguous filtersalong C dimension. The exemplary group 1440 can be described by equation1442, which states W^((k))={w_(i) ^(j)}={w₀ ⁰, w₁ ⁰, w₂ ⁰, . . . , w_(N)⁰}, α_(k)={α_(k) ^(j)}={α_(k) ^(J), . . . }, i=1 . . . N, j=1 . . .(R×S). N filters 1444A-1444N (w₀ . . . w_(N)) contain ternarized weightsthat are derived based on full precision weights. For each group 1440 of3×3 ternary filters, there will be a 3×3 filter containing a scalingfactor 1446 (α) for each element in the filter. Accordingly, for Nfilters, N ternary operations are performed for one multiplication.

Hardware logic can be configured to perform low-precision fixed pointcomputations using a 32-bit accumulator for low precision computations.In one embodiment, a static grouping mechanism is used to ternarizeweights at each convolution layer and quantize the group scaling factorsto 8-bit fixed point values. The activations at each layer are quantizedto 8-bits. One embodiment uses 8-bit precision for weights of the firstconvolution and fully connected layers to prevent loss accumulation.Batch normalization parameters may be computed during the inferencephase to compensate for the shift in variance that quantizationintroduces.

FIG. 14D illustrates a flow diagram of operations associated withcomputational logic, according to an embodiment. The illustratedoperations and logic can be applied before computation to quantize apre-trained full precision model for use in inferencing computation onhardware optimized for INT8 compute. Portions of the logic can also beapplied on a layer-by-layer basis for machine learning computeoperations to re-quantize activation tensors between layers. In oneembodiment conversion logic 1450 includes first conversion sub-logic1453 that accepts as input a single precision floating point (FP32)weight tensor 1451 and second conversion sub-logic 1454 that accepts anFP32 activation tensor 1452. In one embodiment, the first conversionsub-logic 1453 converts the FP32 weight tensor 1451 to an INT8 ternarytensor 1455, while the second conversion sub-logic 1454 converts theinput FP32 activation tensor 1452 to an INT8 tensor 1456. The INT8ternary tensor 1455 has the formulation (α× Ŵ) and includes one or moresets of ternarized (e.g., 2-bit) weights (Ŵ) and one or more 8-bitscaling factors (α). In one embodiment, some instances of the INT8ternary tensor 1455 can use 8-bit weights (Ŵ). For example, the weightsassociated with a first layer and the fully connected layers of a neuralnetwork can use 8-bit weights to reduce the degree of loss accumulationacross subsequent layers.

The quantized weight and activation data can be provided to a lowprecision parallel compute unit 1463. In one embodiment, the parallelcompute unit 1463 performs 32-bit accumulation and can store datainternally within registers having at least 32-bits of precision. Theinternal registers can include an input register 1457 for ternaryweights and an input register 1458 for INT8 activations. The parallelcompute unit 1463 includes logic 1461 to perform a convolution operationthat generates single precision floating point output in the form of anFP32 output tensor. In one embodiment, the FP32 output tensor can bere-quantized by conversion logic 1462 to convert the output activationto INT8 for subsequent processing.

FIG. 14E illustrates hardware logic for a ternarization unit 1465,according to an embodiment. The ternarization unit 1465 can be used toternarized and/or quantize full precision (e.g., 32-bit) weight andactivation data into low precision (e.g., 8-bit) data for input into lowprecision compute units. The ternarization unit 1465 can include aregister file 1466 to store input and output data. Full-precision datacan be stored in memory of a parallel processor that includes theternarization unit 1465. The ternarization unit 1465 can include anoptional cache memory to store portions of full precision model dataretrieved from memory and can optionally include a dedicated memorycontroller 1468 to control access to parallel processor memory. Theternarization unit 1465 includes weight ternarization logic 1472 andactivation quantization logic 1474, for example, to perform conversionof weight and activation data of the full-precision pre-trained model.Output data can then be stored within the register file 1466 forretrieval by software logic having access to the ternarization unit1465. The software logic can then store the quantized model data for usein performing inferencing operations on INT8 optimized machine learninglogic. In one embodiment, the output data can be transmitted via acompute communication fabric 1476 to a set of parallel compute units1478, which may be optimized to perform INT8 machine learningoperations.

While static grouping is described, dynamic grouping may also beimplemented. Dynamic grouping may be implemented within a layer andacross multiple layers. When dynamic grouping is enabled, the datadistribution is analyzed and groups are dynamically generated based onthe changes in the data pattern. For example, where a layer has datathat fits multiple data distributions, multiple groups can be used, withmultiple scaling and threshold values. Using a larger number of groupscan better fit the quantization to the data distribution of thefull-precision weights, increasing the accuracy of the ternary modelrelative to the full-precision model at the trade-off of increasedoverhead due to the larger number of scaling multiplications required.Logic to implement dynamic grouping can be optimized to determine theproper balance between accuracy and performance.

FIG. 14F illustrates logic 1480 to perform fine-grained quantization ofweight data, according to an embodiment. The logic 1480 may beimplemented in software, firmware and/or hardware as described herein.Embodiments implemented via software and firmware may be implemented bycomputer executed instructions stored in one or more non-transitorycomputer readable media such as magnetic, optical or semiconductorstorage. The logic 1480 may also be implemented within or in associationwith a graphics processing unit, such as the GPGPUs described herein.

In one embodiment the logic 1480 performs operations to partitionweights along each input channel as indicated in block 1482. Thepartitioning can be performed in a static or dynamic manner and can beperformed within or across layers of a neural network model. For exampleand in one embodiment weights associated with a machine learning modelcan be partitioned statically in which each layer is associated with apartition. In one embodiment a layer can be dynamically partitioned,such that where multiple portions of the data can be best quantizedaccording to multiple data distributions, a separate partition iscreated for each of the multiple portions.

The logic 1480 can then determine a scaling factor (α) for eachpartition, as indicated in block 1483 and a weight threshold (Δ) can bedetermined for each partition as indicated in block 1484. The scalingfactor and threshold can be determined based on the mathematicalequations listed and described above. The logic 1480 can then performthe quantization for each partition based on the determined scalingfactor (α) and threshold (Δ) values for each partition, as indicated inblock 1485.

Local Optimization Based Ternary Representation

The above approach for weight ternarization without re-training focuseson minimizing the

distance between full-precision weights and the ternary weights. In oneembodiment, logic is configured to ternarize the weights by minimizing

distance on the output of each layer, where the output is a function ofboth weights and inputs. The use of the entire set of training data,which can number in the range of several million units of data, may beresource intensive and reduce the efficiency of the optimization. Toreduce the overall resource requirements, some embodiments make use ofonly a subset of samples that are intelligently selected from theoverall set of training data. For example, embodiments can userepresentative data points from each category of images or othertraining data. In one embodiment, the representative data points canrepresent 10% of the overall training data set.

Where ⊙ indicates a convolution operation. For an input x, output y is:

y=W⊙x

Let

be the set of all inputs, and let x be a subset of selected inputs. Wewant to ternarize W to Ŵ such that:

$\underset{\hat{W}}{\arg\min}{\sum\limits_{x \in X}{{{W \odot x} - {\hat{W} \odot x}}}_{F}^{2}}$

In other words, local optimization determines the ternary weights foreach layer by finding the weights that minimizing the

distance between convolution outputs computed using full precisionweights (W) and ternarized weights (Ŵ).

FIG. 15A illustrates a schematic diagram 1500 illustrating ternarizationlogic 1502 relative to local optimization ternarization logic 1504. Theillustrated logic is further described as logic 1520 in FIG. 15A below,and can be performed by any of the computational logic described herein.

In one embodiment, computational logic can perform an inferenceoperation that includes to compute a convolution output (y) 1503 basedon input data 1501 (e.g., input data at a first layer, output of aprevious layer at subsequent layers) and a set of full-precisionpre-trained weights (W) 1512. The computational logic can also perform aternary inference operation by computing a convolution operation (e.g.,via an XOR operation) using the input data 1501 and ternarizedpre-trained weights (Ŵ) 1514 to generate output (ŷ) 1505. As describedabove, the ternarization logic 1502 can cause computational logic toternarize the full-precision weights 1512 by determining a ternarizationscale factor that minimizes

loss between pre-trained weights and the ternary weights, such that W≈Ŵ.To perform the local optimization, local optimization ternarizationlogic 1504 can configure the computational logic to determine aternarization scale factor the weights that would minimize the

loss between the full precision output 1503 and the ternarized output1505.

FIG. 15B illustrates logic 1520 to perform local optimizationternarization, according to an embodiment. The logic 1520 to performlocal optimization ternarization can be performed by any general-purposegraphics processor as described herein that includes computational logicas described herein.

In one embodiment, the logic 1520 can configure computational logic of amachine learning accelerator to receive a group of full-precisionpre-trained weights, as shown at block 1522. The logic 1520 can thenconfigure the computational logic to analyze an input data set andcorresponding output data set of a convolution operation performed usingthe pre-trained weights, as shown at block 1524. The logic 1520 can thenconfigure the computational logic to ternarize the full-precisionweights by solving the minimization problem

$\underset{\hat{W}}{\arg\min}{\sum_{x \in X}{{{{W \odot x} - {\hat{W} \odot x}}}_{F}^{2}.}}$

Local optimization ternarization can be performed on a layer-by-layerbasis without requiring computationally expensive re-trainingoperations.

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 16-29 illustrate alternativesystems and graphics processing hardware that can implement any and allof the techniques described above.

Additional Exemplary Graphics Processing System Overview

FIG. 16 is a block diagram of a processing system 1600, according to anembodiment. In various embodiments, the system 1600 includes one or moreprocessors 1602 and one or more graphics processors 1608, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1602 or processorcores 1607. In one embodiment, the system 1600 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1600 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1600 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1600 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1600 is a television or set topbox device having one or more processors 1602 and a graphical interfacegenerated by one or more graphics processors 1608.

In some embodiments, the one or more processors 1602 each include one ormore processor cores 1607 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1607 is configured to process aspecific instruction set 1609. In some embodiments, instruction set 1609may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1607 may each processa different instruction set 1609, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1607may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1602 includes cache memory 1604.Depending on the architecture, the processor 1602 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1602. In some embodiments, the processor 1602 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1607 using knowncache coherency techniques. A register file 1606 is additionallyincluded in processor 1602 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1602.

In some embodiments, processor 1602 is coupled with a processor bus 1610to transmit communication signals such as address, data, or controlsignals between processor 1602 and other components in system 1600. Inone embodiment the system 1600 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1616 and an Input Output(I/O) controller hub 1630. A memory controller hub 1616 facilitatescommunication between a memory device and other components of system1600, while an I/O Controller Hub (ICH) 1630 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1616 is integrated within the processor.

Memory device 1620 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment, the memorydevice 1620 can operate as system memory for the system 1600, to storedata 1622 and instructions 1621 for use when the one or more processors1602 executes an application or process. Memory controller hub 1616 alsocouples with an optional external graphics processor 1612, which maycommunicate with the one or more graphics processors 1608 in processors1602 to perform graphics and media operations.

In some embodiments, ICH 1630 enables peripherals to connect to memorydevice 1620 and processor 1602 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1646, afirmware interface 1628, a wireless transceiver 1626 (e.g., Wi-Fi,Bluetooth), a data storage device 1624 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1640 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1642 connect input devices, suchas keyboard and mouse 1644 combinations. A network controller 1634 mayalso couple with ICH 1630. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 1610. It willbe appreciated that the system 1600 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 1630may be integrated within the one or more processor 1602, or the memorycontroller hub 1616 and I/O controller hub 1630 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1612.

FIG. 17 is a block diagram of an embodiment of a processor 1700 havingone or more processor cores 1702A-1702N, an integrated memory controller1714, and an integrated graphics processor 1708. Those elements of FIG.17 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1700 can include additional cores up to and including additional core1702N represented by the dashed lined boxes. Each of processor cores1702A-1702N includes one or more internal cache units 1704A-1704N. Insome embodiments each processor core also has access to one or moreshared cached units 1706.

The internal cache units 1704A-1704N and shared cache units 1706represent a cache memory hierarchy within the processor 1700. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of shared midlevel cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1706 and1704A-1704N.

In some embodiments, processor 1700 may also include a set of one ormore bus controller units 1716 and a system agent core 1710. The one ormore bus controller units 1716 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1710 provides management functionality forthe various processor components. In some embodiments, system agent core1710 includes one or more integrated memory controllers 1714 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1702A-1702Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1710 includes components for coordinating andoperating processor cores 1702A-1702N during multi-threaded processing.System agent core 1710 may additionally include a power control unit(PCU), which includes logic and components to regulate the power stateof processor cores 1702A-1702N and graphics processor 1708.

In some embodiments, processor 1700 additionally includes graphicsprocessor 1708 to execute graphics processing operations. In someembodiments, the graphics processor 1708 couples with the set of sharedcache units 1706, and the system agent core 1710, including the one ormore integrated memory controllers 1714. In some embodiments, a displaycontroller 1711 is coupled with the graphics processor 1708 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1711 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1708 or system agent core 1710.

In some embodiments, a ring-based interconnect 1712 is used to couplethe internal components of the processor 1700. However, an alternativeinterconnect unit may be used, such as a point-to-point interconnect, aswitched interconnect, or other techniques, including techniques wellknown in the art. In some embodiments, graphics processor 1708 coupleswith the ring-based interconnect 1712 via an I/O link 1713.

The exemplary I/O link 1713 represents at least one of multiplevarieties of I/O interconnects, including an on-package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1718, such as an eDRAM module.In some embodiments, each of the processor cores 1702A-1702N andgraphics processor 1708 use embedded memory modules 1718 as a sharedLast Level Cache.

In some embodiments, processor cores 1702A-1702N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1702A-1702N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1702A-1702Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1702A-1702N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1700 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 18 is a block diagram of a graphics processor 1800, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 1800 includesa memory interface 1814 to access memory. Memory interface 1814 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 1800 also includes a displaycontroller 1802 to drive display output data to a display device 1820.Display controller 1802 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 1800includes a video codec engine 1806 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1800 includes a block imagetransfer (BLIT) engine 1804 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 1810. In someembodiments, GPE 1810 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 1810 includes a 3D pipeline 1812 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 1812 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 1815.While 3D pipeline 1812 can be used to perform media operations, anembodiment of GPE 1810 also includes a media pipeline 1816 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 1816 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 1806. In some embodiments, media pipeline 1816 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 1815. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 1815.

In some embodiments, 3D/Media sub-system 1815 includes logic forexecuting threads spawned by 3D pipeline 1812 and media pipeline 1816.In one embodiment, the pipelines send thread execution requests to3D/Media sub-system 1815, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media sub-system 1815 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Graphics Processing Engine

FIG. 19 is a block diagram of a graphics processing engine 1910 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 1910 is a version ofthe GPE 1810 shown in FIG. 18 . Elements of FIG. 19 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 1812 and media pipeline 1816 of FIG. 18 are illustrated. Themedia pipeline 1816 is optional in some embodiments of the GPE 1910 andmay not be explicitly included within the GPE 1910. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 1910.

In some embodiments, GPE 1910 couples with or includes a commandstreamer 1903, which provides a command stream to the 3D pipeline 1812and/or media pipelines 1816. In some embodiments, command streamer 1903is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 1903 receives commands from the memory and sends thecommands to 3D pipeline 1812 and/or media pipeline 1816. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 1812 and media pipeline 1816. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 1812 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 1812 and/or image data andmemory objects for the media pipeline 1816. The 3D pipeline 1812 andmedia pipeline 1816 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 1914.

In various embodiments, the 3D pipeline 1812 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 1914. The graphics core array 1914 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphics core array 1914 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 1914 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general-purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 1607 of FIG. 16 or processor core 1702A-1702N as inFIG. 17 .

Output data generated by threads executing on the graphics core array1914 can output data to memory in a unified return buffer (URB) 1918.The URB 1918 can store data for multiple threads. In some embodiments,the URB 1918 may be used to send data between different threadsexecuting on the graphics core array 1914. In some embodiments, the URB1918 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 1920.

In some embodiments, graphics core array 1914 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 1910. In one embodiment, the executionresources are dynamically scalable, such that execution resources may beenabled or disabled as needed.

The graphics core array 1914 couples with shared function logic 1920that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 1920 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 1914. In variousembodiments, shared function logic 1920 includes but is not limited tosampler 1921, math 1922, and inter-thread communication (ITC) 1923logic. Additionally, some embodiments implement one or more cache(s)1925 within the shared function logic 1920. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 1914. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 1920 and shared amongthe execution resources within the graphics core array 1914. The preciseset of functions that are shared between the graphics core array 1914and included within the graphics core array 1914 varies betweenembodiments.

FIG. 20 is a block diagram of another embodiment of a graphics processor2000. Elements of FIG. 20 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2000 includes a ringinterconnect 2002, a pipeline front-end 2004, a media engine 2037, andgraphics cores 2080A-2080N. In some embodiments, ring interconnect 2002couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2000 receives batches ofcommands via ring interconnect 2002. The incoming commands areinterpreted by a command streamer 2003 in the pipeline front-end 2004.In some embodiments, graphics processor 2000 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2080A-2080N. For 3D geometry processing commands,command streamer 2003 supplies commands to geometry pipeline 2036. Forat least some media processing commands, command streamer 2003 suppliesthe commands to a video front end 2034, which couples with a mediaengine 2037. In some embodiments, media engine 2037 includes a VideoQuality Engine (VQE) 2030 for video and image post-processing and amulti-format encode/decode (MFX) 2033 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2036 and media engine 2037 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2080A.

In some embodiments, graphics processor 2000 includes scalable threadexecution resources featuring modular cores 2080A-2080N (sometimesreferred to as core slices), each having multiple sub-cores 2050A-550N,2060A-2060N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2000 can have any number of graphicscores 2080A through 2080N. In some embodiments, graphics processor 2000includes a graphics core 2080A having at least a first sub-core 2050Aand a second sub-core 2060A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2050A).In some embodiments, graphics processor 2000 includes multiple graphicscores 2080A-2080N, each including a set of first sub-cores 2050A-2050Nand a set of second sub-cores 2060A-2060N. Each sub-core in the set offirst sub-cores 2050A-2050N includes at least a first set of executionunits 2052A-2052N and media/texture samplers 2054A-2054N. Each sub-corein the set of second sub-cores 2060A-2060N includes at least a secondset of execution units 2062A-2062N and samplers 2064A-2064N. In someembodiments, each sub-core 2050A-2050N, 2060A-2060N shares a set ofshared resources 2070A-2070N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Execution Units

FIG. 21 illustrates thread execution logic 2100 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 21 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 2100 includes a shaderprocessor 2102, a thread dispatcher 2104, instruction cache 2106, ascalable execution unit array including a plurality of execution units2108A-2108N, a sampler 2110, a data cache 2112, and a data port 2114. Inone embodiment the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 2108A, 2108B, 2108C, 2108D, through 2108N-1 and 2108N)based on the computational requirements of a workload. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 2100 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache2106, data port 2114, sampler 2110, and execution units 2108A-2108N. Insome embodiments, each execution unit (e.g. 2108A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 2108A-2108N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 2108A-2108N are primarily usedto execute shader programs. A shader processor 2102 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2104. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2108A-2108N. For example, the geometry pipeline (e.g., 2036 of FIG. 20 )can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 2100 (FIG. 21 ) for processing. In some embodiments,thread dispatcher 2104 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 2108A-2108N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2108A-2108N is capable of multi-issue single instruction multipledata (SIMD) execution and multi threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2108A-2108N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 2108A-2108N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2108A-2108N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 2106) are included in thethread execution logic 2100 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2112) are included to cache thread data during thread execution. In someembodiments, a sampler 2110 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2110 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2100 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2102 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2102 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2102dispatches threads to an execution unit (e.g., 2108A) via threaddispatcher 2104. In some embodiments, shader processor 2102 uses texturesampling logic in the sampler 2110 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 2114 provides a memory accessmechanism for the thread execution logic 2100 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2114 includes or couples to one or more cachememories (e.g., data cache 2112) to cache data for memory access via thedata port.

FIG. 22 is a block diagram illustrating graphics processor instructionformats 2200 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2200 described and illustrated are macroinstructions, in that they are instructions supplied to the executionunit, as opposed to micro-operations resulting from instruction decodeonce the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2210. A 64-bitcompacted instruction format 2230 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2210 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 2230. The native instructions availablein the 64-bit format 2230 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 2213. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format2210.

For each format, instruction opcode 2212 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2214 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 2210 an exec-size field2216 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 2216 is not available foruse in the 64-bit compact instruction format 2230.

Some execution unit instructions have up to three operands including twosource operands, src0 2220, src1 2222, and one destination 2218. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2224), where the instructionopcode 2212 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2210 includes anaccess/address mode field 2226 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 2210 includes anaccess/address mode field 2226, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 2226 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 2212bit-fields to simplify Opcode decode 2240. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2242 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic opcode group 2242 shares the five most significant bits(MSB), where move (mov) instructions are in the form of 0000xxxxb andlogic instructions are in the form of 0001xxxxb. A flow controlinstruction group 2244 (e.g., call, jump (jmp)) includes instructions inthe form of 0010xxxxb (e.g., 0x20). A miscellaneous instruction group2246 includes a mix of instructions, including synchronizationinstructions (e.g., wait, send) in the form of 0011xxxxb (e.g., 0x30). Aparallel math instruction group 2248 includes component-wise arithmeticinstructions (e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g.,0x40). The parallel math group 2248 performs the arithmetic operationsin parallel across data channels. The vector math group 2250 includesarithmetic instructions (e.g., dp4) in the form of 0101xxxxb (e.g.,0x50). The vector math group performs arithmetic such as dot productcalculations on vector operands.

Graphics Pipeline

FIG. 23 is a block diagram of another embodiment of a graphics processor2300. Elements of FIG. 23 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2300 includes a graphicspipeline 2320, a media pipeline 2330, a display engine 2340, threadexecution logic 2350, and a render output pipeline 2370. In someembodiments, graphics processor 2300 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2300 via a ring interconnect 2302. In someembodiments, ring interconnect 2302 couples graphics processor 2300 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 2302 areinterpreted by a command streamer 2303, which supplies instructions toindividual components of graphics pipeline 2320 or media pipeline 2330.

In some embodiments, command streamer 2303 directs the operation of avertex fetcher 2305 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2303. In someembodiments, vertex fetcher 2305 provides vertex data to a vertex shader2307, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 2305 andvertex shader 2307 execute vertex-processing instructions by dispatchingexecution threads to execution units 2352A-2352B via a thread dispatcher2331.

In some embodiments, execution units 2352A-2352B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2352A-2352B have anattached L1 cache 2351 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 2320 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 2313 operatesat the direction of hull shader 2311 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 2320. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 2311, tessellator 2313, and domain shader 2317) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2319 via one or more threads dispatched to executionunits 2352A-2352B, or can proceed directly to the clipper 2329. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader2319 receives input from the vertex shader 2307. In some embodiments,geometry shader 2319 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2329 processes vertex data. The clipper2329 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 2373 in the render output pipeline2370 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 2350. In some embodiments, anapplication can bypass the rasterizer and depth test component 2373 andaccess un-rasterized vertex data via a stream out unit 2323.

The graphics processor 2300 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2352A-2352B and associated cache(s) 2351,texture and media sampler 2354, and texture/sampler cache 2358interconnect via a data port 2356 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2354, caches 2351, 2358 and execution units2352A-2352B each have separate memory access paths.

In some embodiments, render output pipeline 2370 contains a rasterizerand depth test component 2373 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache2378 and depth cache 2379 are also available in some embodiments. Apixel operations component 2377 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 2341, or substituted at display time by the displaycontroller 2343 using overlay display planes. In some embodiments, ashared L3 cache 2375 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 2330 includes amedia engine 2337 and a video front-end 2334. In some embodiments, videofront-end 2334 receives pipeline commands from the command streamer2303. In some embodiments, media pipeline 2330 includes a separatecommand streamer. In some embodiments, video front-end 2334 processesmedia commands before sending the command to the media engine 2337. Insome embodiments, media engine 2337 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2350 via thread dispatcher 2331.

In some embodiments, graphics processor 2300 includes a display engine2340. In some embodiments, display engine 2340 is external to processor2300 and couples with the graphics processor via the ring interconnect2302, or some other interconnect bus or fabric. In some embodiments,display engine 2340 includes a 2D engine 2341 and a display controller2343. In some embodiments, display engine 2340 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2343 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 2320 and media pipeline 2330 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 24A is a block diagram illustrating a graphics processor commandformat 2400 according to some embodiments. FIG. 24B is a block diagramillustrating a graphics processor command sequence 2410 according to anembodiment. The solid lined boxes in FIG. 24A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 2400 of FIG. 24A includes data fields to identify atarget client 2402 of the command, a command operation code (opcode)2404, and the relevant data field 2406 for the command. A sub-opcode2405 and a command size 2408 are also included in some commands.

In some embodiments, client 2402 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 2404 and, if present, sub-opcode 2405 to determine theoperation to perform. The client unit performs the command usinginformation in data field 2406. For some commands an explicit commandsize 2408 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 24B shows an exemplary graphics processorcommand sequence 2410. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 2410 maybegin with a pipeline flush command 2412 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 2422 and the media pipeline 2424 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 2412 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 2413 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 2413is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 2412 isrequired immediately before a pipeline switch via the pipeline selectcommand 2413.

In some embodiments, a pipeline control command 2414 configures agraphics pipeline for operation and is used to program the 3D pipeline2422 and the media pipeline 2424. In some embodiments, pipeline controlcommand 2414 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 2414 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 2416 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 2416 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 2420,the command sequence is tailored to the 3D pipeline 2422 beginning withthe 3D pipeline state 2430 or the media pipeline 2424 beginning at themedia pipeline state 2440.

The commands to configure the 3D pipeline state 2430 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 2430 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 2432 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 2432 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 2432command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 2432 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 2422 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 2422 is triggered via an execute 2434command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 2410follows the media pipeline 2424 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 2424 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 2424 is configured in a similarmanner as the 3D pipeline 2422. A set of commands to configure the mediapipeline state 2440 are dispatched or placed into a command queue beforethe media object commands 2442. In some embodiments, the commands forthe media pipeline state 2440 include data to configure the mediapipeline elements that will be used to process the media objects. Thisincludes data to configure the video decode and video encode logicwithin the media pipeline, such as encode or decode format. In someembodiments, commands for the media pipeline state 2440 enable supportthe use of one or more pointers to “indirect” state elements thatcontain a batch of state settings.

In some embodiments, media object commands 2442 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 2442. Once the pipeline state is configured andmedia object commands 2442 are queued, the media pipeline 2424 istriggered via an execute command 2444 or an equivalent execute event(e.g., register write). Output from media pipeline 2424 may then be postprocessed by operations provided by the 3D pipeline 2422 or the mediapipeline 2424. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 25 illustrates exemplary graphics software architecture for a dataprocessing system 2500 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application2510, an operating system 2520, and at least one processor 2530. In someembodiments, processor 2530 includes a graphics processor 2532 and oneor more general-purpose processor core(s) 2534. The graphics application2510 and operating system 2520 each execute in the system memory 2550 ofthe data processing system.

In some embodiments, 3D graphics application 2510 contains one or moreshader programs including shader instructions 2512. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 2514 in a machinelanguage suitable for execution by the general-purpose processor core(s)2534. The application also includes graphics objects 2516 defined byvertex data.

In some embodiments, operating system 2520 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 2520 can support agraphics API 2522 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 2520uses a front-end shader compiler 2524 to compile any shader instructions2512 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 2510. In some embodiments, the shader instructions 2512 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 2526 contains a back-endshader compiler 2527 to convert the shader instructions 2512 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 2512 in the GLSL high-level language are passed to a usermode graphics driver 2526 for compilation. In some embodiments, usermode graphics driver 2526 uses operating system kernel mode functions2528 to communicate with a kernel mode graphics driver 2529. In someembodiments, kernel mode graphics driver 2529 communicates with graphicsprocessor 2532 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 26 is a block diagram illustrating an IP core development system2600 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2600 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2630 can generate a software simulation 2610 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation2610 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2612. The simulation model 2612 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2615 can then be created or synthesized from thesimulation model 2612. The RTL design 2615 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2615, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2615 or equivalent may be further synthesized by thedesign facility into a hardware model 2620, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2665 using non-volatile memory 2640 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2650 or wireless connection 2660. Thefabrication facility 2665 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIG. 27-29 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 27 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2700 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 2700includes one or more application processor(s) 2705 (e.g., CPUs), atleast one graphics processor 2710, and may additionally include an imageprocessor 2715 and/or a video processor 2720, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 2700 includes peripheral or bus logic including a USBcontroller 2725, UART controller 2730, an SPI/SDIO controller 2735, andan I²S/I²C controller 2740. Additionally, the integrated circuit caninclude a display device 2745 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2750 and a mobileindustry processor interface (MIPI) display interface 2755. Storage maybe provided by a flash memory subsystem 2760 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2765 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2770.

FIG. 28 is a block diagram illustrating an exemplary graphics processor2810 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 2810 can be a variant of the graphics processor 2710 of FIG.27 . Graphics processor 2810 includes a vertex processor 2805 and one ormore fragment processor(s) 2815A-2815N (e.g., 2815A, 2815B, 2815C,2815D, through 2815N-1, and 2815N). Graphics processor 2810 can executedifferent shader programs via separate logic, such that the vertexprocessor 2805 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 2815A-2815Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 2805 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 2815A-2815N use the primitiveand vertex data generated by the vertex processor 2805 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 2815A-2815N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 2810 additionally includes one or more memorymanagement units (MMUs) 2820A-2820B, cache(s) 2825A-2825B, and circuitinterconnect(s) 2830A-2830B. The one or more MMU(s) 2820A-2820B providefor virtual to physical address mapping for the graphics processor 2810,including for the vertex processor 2805 and/or fragment processor(s)2815A-2815N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 2825A-2825B. In one embodiment, the one or more MMU(s)2820A-2820B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 2705, image processor 2715, and/or video processor 2720 ofFIG. 27 , such that each processor 2705-2720 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 2830A-2830B enable graphics processor 2810 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 29 is a block diagram illustrating an additional exemplary graphicsprocessor 2910 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 2910 can be a variant of the graphics processor 2710of FIG. 27. Graphics processor 2910 includes the one or more MMU(s)2820A-2820B, cache(s) 2825A-2825B, and circuit interconnect(s)2830A-2830B of the integrated circuit 2800 of FIG. 28 .

Graphics processor 2910 includes one or more shader core(s) 2915A-2915N(e.g., 2915A, 2915B, 2915C, 2915D, 2915E, 2915F, through 2915N-1, and2915N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 2910 includes an inter-core taskmanager 2905, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 2915A-2915N and a tiling unit 2918to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

One embodiment provides for a computing device comprising a parallelprocessor compute unit to perform a set of parallel integer computeoperations; a ternarization unit including a weight ternarizationcircuit and an activation quantization circuit; wherein the weightternarization circuit is to convert a weight tensor from afloating-point representation to a ternary representation including aternary weight and a scale factor; wherein the activation quantizationcircuit is to convert an activation tensor from a floating-pointrepresentation to an integer representation; and wherein the parallelprocessor compute unit includes one or more circuits to perform the setof parallel integer compute operations on the ternary representation ofthe weight tensor and the integer representation of the activationtensor.

One embodiment provides for a method comprising ternarizing a group ofpre-trained weights; determining if a residual is required for the groupof pre-training weights; computing the residual by taking the differencebetween pre-trained weights of this group and the ternary weights;ternarizing the residual weights; and storing the ternarized residualweights. One embodiment comprises a non-transitory machine-readablemedium storing instructions which, when executed by one or moreprocessors of a computing device, cause the computing device to performoperations comprising any one of the methods described herein.

One embodiment comprises a data processing system comprising memory tostore instructions to perform ternary quantization on weight data of anartificial neural network and one or more parallel processors to executethe instructions stored in memory, the instructions to cause the one ormore parallel processors to ternarizing a group of pre-trained weights;determining if a residual is required for the group of pre-trainingweights; computing the residual by determining an arithmetic differencebetween values of pre-trained weights of the group and values of theternary weights; ternarizing the residual weights; and storing theternarized residual weights.

One embodiment provides a non-transitory computer-readable mediumstoring instructions, execution of which by one or more processorsincluding a general-purpose graphics processor, causes the one or moreprocessors to perform operations comprising determining a first scalefactor to apply to first tensor data associated with a first layer of aneural network model, converting the first tensor data associated withthe first layer of the neural network model to generate first convertedtensor data, the first tensor data converted from a first datatype to asecond datatype, wherein the second datatype is an 8-bit datatype,generating a first output tensor that is associated with the first layerof the neural network model, the first output tensor generated via thegeneral-purpose graphics processor based on the first converted tensordata and the first scale factor, determining a second scale factor toapply to second tensor data associated with a second layer of the neuralnetwork model, converting the second tensor data associated with thesecond layer of the neural network model to generate second convertedtensor data, the second tensor data converted from the first datatype tothe second datatype, and generating a second output tensor that isassociated with the second layer of the neural network model, the secondoutput tensor generated via the general-purpose graphics processor basedon the second converted tensor data and the second scale factor.

The embodiments described herein refer to specific configurations ofhardware, such as application specific integrated circuits (ASICs),configured to perform certain operations or having a predeterminedfunctionality. Such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage devices of a given electronic device typically storecode and/or data for execution on the set of one or more processors ofthat electronic device.

Of course, one or more parts of an embodiment may be implemented usingdifferent combinations of software, firmware, and/or hardware.Throughout this detailed description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the embodiments may be practiced withoutsome of these specific details. In certain instances, well-knownstructures and functions were not described in elaborate detail to avoidobscuring the inventive subject matter of the embodiments. Accordingly,the scope and spirit of the invention should be judged in terms of theclaims that follow.

What is claimed is:
 1. A non-transitory computer-readable medium storinginstructions, execution of which by one or more processors including ageneral-purpose graphics processor, causes the one or more processors toperform operations comprising: determining a first scale factor to applyto first tensor data associated with a first layer of a neural networkmodel; converting the first tensor data associated with the first layerof the neural network model to generate first converted tensor data, thefirst tensor data converted from a first datatype to a second datatype,wherein the second datatype is an 8-bit datatype; generating a firstoutput tensor that is associated with the first layer of the neuralnetwork model, the first output tensor generated via the general-purposegraphics processor based on the first converted tensor data and thefirst scale factor, determining a second scale factor to apply to secondtensor data associated with a second layer of the neural network model;converting the second tensor data associated with the second layer ofthe neural network model to generate second converted tensor data, thesecond tensor data converted from the first datatype to the seconddatatype; and generating a second output tensor that is associated withthe second layer of the neural network model, the second output tensorgenerated via the general-purpose graphics processor based on the secondconverted tensor data and the second scale factor.
 2. The non-transitorycomputer-readable medium of claim 1, wherein the first datatype is afloating-point datatype.
 3. The non-transitory computer-readable mediumof claim 2, wherein the first datatype is a 32-bit floating-pointdatatype.
 4. The non-transitory computer-readable medium of claim 2,wherein the first scale factor is determined to reduce a differencebetween values of the first output tensor when generated via the firsttensor data relative to the values of the first output tensor whengenerated via the first converted tensor data.
 5. The non-transitorycomputer-readable medium of claim 2, wherein the second scale factor isdetermined to reduce a difference between values of the second outputtensor when generated via the second tensor data relative to the valuesof the second output tensor when generated via the second convertedtensor data.
 6. The non-transitory computer-readable medium of claim 1,wherein the first tensor data includes weight values associated with thefirst layer of the neural network model.
 7. The non-transitorycomputer-readable medium of claim 6, wherein the second tensor dataincludes weight values associated with the second layer of the neuralnetwork model.
 8. The non-transitory computer-readable medium of claim7, the operations further comprising: converting a first inputactivation tensor associated with the first layer of the neural networkmodel to generate first converted input activation tensor; andgenerating the first output tensor based in part on the first convertedinput activation tensor.
 9. The non-transitory computer-readable mediumof claim 8, the operations further comprising generating the secondoutput tensor based at least in part on the first output tensor and thesecond converted tensor data.
 10. The non-transitory computer-readablemedium of claim 9, the operations further comprising: generating thefirst output tensor in the first datatype; and converting the firstoutput tensor from the first datatype to the second datatype beforegenerating the second output tensor.
 11. An apparatus comprising: amemory device; and one or more processors coupled with the memorydevice, the one or more processors including a general-purpose graphicsprocessor, the one or more processors comprising circuitry configuredto: determine a first scale factor to apply to first tensor dataassociated with a first layer of a neural network model; convert thefirst tensor data associated with the first layer of the neural networkmodel to generate first converted tensor data, the first tensor dataconverted from a first datatype to a second datatype, wherein the seconddatatype is an 8-bit datatype; generate a first output tensor that isassociated with the first layer of the neural network model, the firstoutput tensor generated via the general-purpose graphics processor basedon the first converted tensor data and the first scale factor, determinea second scale factor to apply to second tensor data associated with asecond layer of the neural network model; convert the second tensor dataassociated with the second layer of the neural network model to generatesecond converted tensor data, the second tensor data converted from thefirst datatype to the second datatype; and generate a second outputtensor that is associated with the second layer of the neural networkmodel, the second output tensor generated via the general-purposegraphics processor based on the second converted tensor data and thesecond scale factor.
 12. The apparatus of claim 11, wherein the firstdatatype is a floating-point datatype.
 13. The apparatus of claim 12,wherein the first datatype is a 32-bit floating-point datatype.
 14. Theapparatus of claim 12, wherein the first scale factor is determined toreduce a difference between values of the first output tensor whengenerated via the first tensor data relative to the values of the firstoutput tensor when generated via the first converted tensor data. 15.The apparatus of claim 12, wherein the second scale factor is determinedto reduce a difference between values of the second output tensor whengenerated via the second tensor data relative to the values of thesecond output tensor when generated via the second converted tensordata.
 16. The apparatus of claim 11, wherein the first tensor dataincludes weight values associated with the first layer of the neuralnetwork model.
 17. The apparatus of claim 16, wherein the second tensordata includes weight values associated with the second layer of theneural network model.
 18. The apparatus of claim 17, the circuitryconfigured to: convert a first input activation tensor associated withthe first layer of the neural network model to generate first convertedinput activation tensor; and generate the first output tensor based inpart on the first converted input activation tensor.
 19. The apparatusof claim 18, the circuitry configured to generate the second outputtensor based at least in part on the first output tensor and the secondconverted tensor data.
 20. The apparatus of claim 19, the circuitryconfigured to: generate the first output tensor in the first datatype;and convert the first output tensor from the first datatype to thesecond datatype before generating the second output tensor.
 21. Ageneral-purpose graphics processor comprising: a system interface; and amachine learning accelerator circuitry coupled with the systeminterface, the machine learning accelerator circuitry configured to:convert first tensor data associated with a first layer of a neuralnetwork model to generate first converted tensor data, the first tensordata converted from a first datatype to a second datatype, wherein thesecond datatype is an 8-bit datatype; generate a first output tensorthat is associated with the first layer of the neural network model, thefirst output tensor generated via the general-purpose graphics processorbased on the first converted tensor data and a first scale factor, thefirst scale factor determined for the first tensor data; convert secondtensor data associated with a second layer of the neural network modelto generate second converted tensor data, the second tensor dataconverted from the first datatype to the second datatype; and generate asecond output tensor that is associated with the second layer of theneural network model, the second output tensor generated via thegeneral-purpose graphics processor based on the second converted tensordata and a second scale factor, the second scale factor determined forthe second tensor data.
 22. The general-purpose graphics processor ofclaim 21, wherein the first datatype is a floating-point datatype. 23.The general-purpose graphics processor of claim 22, wherein the firstdatatype is a 32-bit floating-point datatype.
 24. The general-purposegraphics processor of claim 22, wherein the first scale factor isdetermined to reduce a difference between values of the first outputtensor when generated via the first tensor data relative to the valuesof the first output tensor when generated via the first converted tensordata.
 25. The general-purpose graphics processor of claim 22, whereinthe second scale factor is determined to reduce a difference betweenvalues of the second output tensor when generated via the second tensordata relative to the values of the second output tensor when generatedvia the second converted tensor data.